Automated NMR Assignment and Protein Structure

描述（由申请人提供）：虽然自动化正在彻底改变生物学的许多方面，但三维蛋白质结构的测定仍然是一项长期，困难和昂贵的任务。为了在更大范围内应用现代技术，如基于结构的药物设计，需要高通量结构基因组学。传统的（半）自动化方法通过核磁共振（NMR）光谱来测定蛋白质结构需要几十次实验和几个月的光谱仪时间，这使得它们不适合高通量自动化。利用核磁共振测定三维蛋白质结构的主要瓶颈之一是生物聚合物中原子的化学位移分配。因此，使用NMR进行高通量结构确定需要对分配问题进行系统的攻击。提出了一种新的算法技术，用于从稀疏的、未分配的NMR数据中自动分配和确定蛋白质结构，基于一种称为Jigsaw的方法。拟议的研究旨在尽量减少必须进行的核磁共振实验的数量和类型，以及解释实验结果所需的人力，同时仍然产生准确的蛋白质结构分析。为了实现高通量数据收集，所提出的方法仅利用少数快速，廉价的NMIR实验。这项研究将建立在Jigsaw的基础上，开发一种极简方法，展示几个关键光谱中可用的大量信息，以及如何使用组合和几何算法的组合来提取这些信息。将开发新的算法和计算机系统，仅从四个核磁共振波谱来确定蛋白质结构。该系统将使用类似于物理几何算法、模式识别和机器视觉、信号处理和机器人的算法，以分析光谱，将光谱峰分配给原子相互作用，计算二级结构，并估计全局折叠。Jigsaw将扩展到更大的蛋白质上，并在实验核磁共振数据上进行测试。一个新的概率框架将被实现来处理增加的光谱复杂性和稀疏的信息内容，无论是在较大的蛋白质，还是在高通量核磁共振协议中。

DESCRIPTION (provided by applicant): While automation is revolutionizing many aspects of biology, the determination of three-dimensional protein structure remains a long, hard, and expensive task. High-throughput structural genomics is required in order to apply modem techniques such as structure-based drug design on a much larger scale. Traditional (semi-) automated approaches to protein structure determination through nuclear magnetic resonance (NMR) spectroscopy require dozens of experiments and months of spectrometer time, making them unsuitable for high-throughput automation. One of the main bottlenecks in the determination of three-dimensional protein structures by NMR is the assignment of chemical shifts to atoms in a biopolymer. Therefore, high-throughput structure determination using NMR requires a systematic attack on the assignment problem. Novel algorithmic techniques are proposed for automated assignment and protein structure determination from sparse, unassigned NMR data, based on an approach called Jigsaw. The proposed research aims to minimize the number and types of NMR experiments that must be performed and the amount of human effort required to interpret the experimental results, while still producing an accurate analysis of the protein structure. To enable high-throughput data collection, the proposed methods utilize only a few fast, cheap NMIR experiments. The research will build on Jigsaw to develop a minimalist approach, demonstrating the large amount of information available in a few key spectra, and how it can be extracted using a combination of combinatorial and geometric algorithms. New algorithms and computer systems will be developed for determining protein structure from only four NMR spectra. The system will use algorithms similar to and adapted from physical geometric algorithms, pattern recognition and machine vision, signal processing, and robotics, in order to analyze spectra, assign spectral peaks to atom interactions, compute secondary structure, and estimate the global fold. Jigsaw will be extended to work on larger proteins, and tested on experimental NMR data. A novel probabilistic framework will be implemented to handle the increased spectral complexity and sparser information content obtained both for larger proteins, and in high-throughput NMR protocols.